Code Generation

Write prompts that reduce hallucinations and get precise code on the first try

Stop debugging AI-generated code that ignores your constraints. Get prompts with context, types, error handling, and test requirements built in.

73%

Fewer hallucinations

Context-first prompts vs bare instructions

40%

Fewer tokens used

Precise prompts reduce back-and-forth

1-2vs 5-8

Iterations to working code

Structured prompts vs vague requests

See the code AI generates

Same task, same AI model. The only difference is the prompt.

Basic prompt
typescript

help me write a function to sort users

function sortUsers(users, field) {
  return users.sort((a, b) => {
    if (a[field] > b[field]) return 1;
    if (a[field] < b[field]) return -1;
    return 0;
  });
}

// Usage
const sorted = sortUsers(data, "name");
Enhanced by Rephrase
typescript

Structured prompt with context & constraints

function sortUsers(
  users: User[],
  sortBy: SortField,
  direction: 'asc' | 'desc' = 'asc'
): User[] {
  const planOrder = { free: 0, pro: 1, enterprise: 2 };

  return [...users].sort((a, b) => {
    const va = a[sortBy], vb = b[sortBy];
    if (va == null) return 1;
    if (vb == null) return -1;

    let result: number;
    if (sortBy === 'plan') {
      result = planOrder[va] - planOrder[vb];
    } else if (sortBy === 'createdAt') {
      result = Date.parse(va) - Date.parse(vb);
    } else {
      result = String(va).localeCompare(String(vb));
    }
    return direction === 'desc' ? -result : result;
  });
}
What went wrong
  • No type safety — untyped parameters and return value
  • Mutates original array with .sort() instead of copy
  • No sort direction — always ascending
  • Crashes on null or undefined field values
What improved
  • Full TypeScript with User[] and SortField types
  • Immutable — spreads array before sorting
  • Custom enum ordering for plan tiers
  • Null-safe with consistent null placement
Same AI, same task. Structured prompts produce production-ready code on the first try.

See the transformation

Your one-liner becomes a structured prompt with context, types, constraints, and test requirements.

Your prompt

help me write a function to sort users

Rephrase suggests

Write a user sorting function in TypeScript for a React application using TanStack Table that handles multiple sort criteria with user-configurable direction. Context: - User data structure: { id: string (uuid), name: string, email: string, createdAt: string (ISO date), plan: 'free' | 'pro' | 'enterprise', isActive: boolean } - Dataset size: < 1,000 users, no pagination required - Integrates with TanStack Table's SortingState Requirements: 1. Function signature: sortUsers(users: User[], sortBy: SortField, sortDirection: 'asc' | 'desc'): User[] 2. Sort by name/email: case-insensitive alphabetical 3. Sort by createdAt: chronological 4. Sort by plan: custom order (free < pro < enterprise) 5. Return sorted array without mutating original Error Handling: - Invalid sortBy -> throw TypeError with descriptive message - Null/undefined users -> return empty array - Missing fields -> treat as null for comparison Include: Type definitions, JSDoc comments, unit test cases covering each sort field and both directions.

For developers

Context-first prompt structure

Rephrase structures every code prompt with context first, so the AI understands your codebase before generating.

1Context

Your existing code, stack, and patterns

TypeScript + React + TanStack Table

2Task

The specific objective

Sort users by multiple criteria

3Constraints

Error handling, types, tests, conventions

No mutation, throw on invalid input

Works in every editor and IDE

Use Rephrase from VS Code, Cursor, terminal, or any app. No plugins needed.

Editors & IDEs

VS CodeCursorXcodeJetBrainsTerminalAny text editor

AI Models

Claude (Opus 4.5 / Sonnet 4.5)ChatGPT (GPT-5.2 / Codex)GitHub CopilotGemini 3 Pro

Open Models

Devstral 2DeepSeek V3.2Qwen3-Coder

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Frequently Asked Questions

Rephrase adds explicit context to your prompts: existing code patterns, type definitions, framework versions, and constraints. When an AI model knows your exact stack and requirements, it hallucinates less because it has concrete boundaries instead of guessing.

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